import time 
start_time = time.time()

import pandas as pd
import geopandas as gpd
import os
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from esda.moran import Moran
from esda.getisord import G_Local
from libpysal.weights import Queen
from tabulate import tabulate
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram
from scipy.stats import chi2_contingency

# Import crime data

recent_df = pd.read_csv('https://raw.githubusercontent.com/YYY677/DSSS/refs/heads/main/Assessment/data/MPS%20LSOA%20Level%20Crime%20(most%20recent%2024%20months).csv')
historical_df = pd.read_csv('https://raw.githubusercontent.com/YYY677/DSSS/refs/heads/main/Assessment/data/MPS%20LSOA%20Level%20Crime%20(Historical).csv')

# Lets merge these two datasets

df = pd.merge(historical_df, recent_df, on=['LSOA Code', 'LSOA Name', 'Borough', 'Major Category', 'Minor Category'], how='outer').fillna(0).sort_index(axis = 1)
df.head()

# calculating the total number of crimes for each year (2011-2024) 
# and aggregates the data by 'LSOA Code', 'Borough', and 'Major Category'.

df_yearly = df.copy()

# Only using data 2014 to 2024
years = range(2011, 2025)

# Iterate through each year and calculate the total
for year in years:
    # Dynamically obtains column names for each year
    cols_to_sum = [col for col in df_yearly.columns if str(year) in str(col)]
    
    # Calculate the total number of crimes for each year
    df_yearly[str(year)] = df_yearly[cols_to_sum].sum(axis=1)

# Create a new dataframe that contains data for each year
df_yearly = df_yearly[["LSOA Code", "Borough", "Major Category"] + [str(year) for year in years]]
df_yearly = df_yearly.groupby(['LSOA Code', "Borough", 'Major Category']).sum().reset_index()

# show the result
df_yearly.head()

#load in the LSOA map 

UK_LSOA = gpd.read_file("https://github.com/YYY677/DSSS/raw/refs/heads/main/Assessment/data/LSOA.gpkg")
#calculate the area of each lsoa
UK_LSOA['area_km2'] = UK_LSOA.area*10**-6
UK_LSOA.head()

# Timeseries of each Major Category. What are trends of each major category like?
drop_list = ['LSOA Code', 'Borough']
cate_grouped_yearly = df_yearly.groupby(by=['Major Category']).agg('sum').drop(columns=drop_list)

cate_grouped_yearly.head()

# Get years
years = cate_grouped_yearly.columns.astype(int)

# Calculating the total count of crimes in each year
total_crimes = cate_grouped_yearly.sum()

# Plot trends for total count of crimes
plt.figure(figsize=(12, 8))
plt.plot(years, total_crimes, marker='o', linestyle='-', color='black', linewidth=2, label='Total Crimes')

# Plot trends for individual crime types
for category in cate_grouped_yearly.index:
    plt.plot(years, cate_grouped_yearly.loc[category], marker='o', linestyle='--', label=category, alpha=0.7)

plt.title("Total Crime Trend and Individual Categories", fontsize=14)
plt.xlabel("Year", fontsize=12)
plt.ylabel("Number of Crimes", fontsize=12)
plt.legend(fontsize=9, loc='upper right', bbox_to_anchor=(1.2, 1))
plt.grid(True, linestyle="--", alpha=0.6)
plt.tight_layout()
plt.show()

# Select only numeric columns (i.e., monthly crime counts)
numeric_cols = df.select_dtypes(include=['number']).columns

# Get all unique major crime categories
major_categories = df['Major Category'].unique()

# Loop through each crime category and plot one figure per category
for cat in major_categories:
    # Filter data for the current crime category
    filtered_data = df[df['Major Category'] == cat]

    # Re-confirm numeric columns within the filtered data
    numeric_cols = filtered_data.select_dtypes(include=['number']).columns

    # Sum crime counts across all LSOAs for each month
    monthly_sum = filtered_data[numeric_cols].sum()

    # Convert to DataFrame
    grouped_df = monthly_sum.reset_index()
    grouped_df.columns = ['Date', 'Count']

    # Extract month and year from the date string (e.g., "202401")
    grouped_df['Month'] = grouped_df['Date'].str[4:6].astype(int)
    grouped_df['Year'] = grouped_df['Date'].str[:4]

    # Pivot for heatmap: rows are months, columns are years
    monthly_pivot = grouped_df.pivot(index='Month', columns='Year', values='Count')

    # Plot heatmap
    plt.figure(figsize=(10, 6))
    sns.heatmap(monthly_pivot, cmap="YlGnBu", linewidths=.5)
    plt.title(f'Monthly Distribution of {cat} Crimes in London', fontsize=15)
    plt.xlabel('Year', fontsize=12)
    plt.ylabel('Month', fontsize=12)
    plt.tight_layout()
    plt.show()

# Function to plot crime rate distribution maps
def plot_crime_rate_by_lsoa(years, major_cat, n_quantiles=7):
    """
    Function to plot crime rate per LSOA for each year.

    Parameters:
    years: List of years to plot.
    major_cat: Crime category to plot.
    n_quantiles: Number of quantiles to split crime rate distribution.
    """
    filtered_data = df_yearly[df_yearly['Major Category'] == major_cat].copy()

    # Merge crime data with geographic data
    merged_cat = UK_LSOA.merge(filtered_data, left_on='lsoa21cd', right_on='LSOA Code', how='left')

    # Calculate area-normalized crime rate (crimes per km²) for each LSOA
    for year in years:
        merged_cat[f'{year}_area'] = merged_cat[str(year)] / merged_cat['area_km2']

    # Set layout: 3 columns per row
    ncols = 3
    num_years = len(years)
    nrows = -(-num_years // ncols)  # Ceiling division
    fig, axes = plt.subplots(nrows, ncols, figsize=(ncols * 5, nrows * 4.5))
    axes = axes.flatten()

    # Plot maps for each year
    for i, year in enumerate(years):
        ax = axes[i]
        year_str = f'{year}_area'

        # Drop NaNs for quantile calculation
        year_data = merged_cat[year_str].dropna()

        # Compute quantile-based bins
        _, bins = pd.qcut(year_data, q=n_quantiles, retbins=True, duplicates="drop")
        unique_cutpoints = np.unique(bins.round(2))
        unique_cutpoints = unique_cutpoints[1:-1]  # Drop min/max edge bins

        # Plot map
        merged_cat.plot(
            column=year_str, cmap='RdBu_r', linewidth=0, ax=ax,
            legend=True, scheme='user_defined',
            classification_kwds={'bins': unique_cutpoints},
            legend_kwds={
                'fontsize': 9,
                'markerscale': 0.5,
                'loc': 'lower right',
                'frameon': True,
                'title': 'Rate'
            },
            missing_kwds={
                'color': 'lightgrey',
                'hatch': '///',
                'label': 'Missing'
            }
        )

        ax.set_title(f'{major_cat} Rate\n{year} (per km²)', fontsize=12)
        ax.axis('off')

    # Remove unused subplots if any
    for i in range(num_years, len(axes)):
        fig.delaxes(axes[i])

    # Adjust spacing between subplots
    plt.subplots_adjust(hspace=0, wspace=0)
    plt.tight_layout()
    plt.show()

years = ['2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021', '2022', '2023', '2024']
major_categories = df_yearly['Major Category'].unique()
for major_cat in major_categories:
  plot_crime_rate_by_lsoa(years, major_cat)

def for_all_types(lsoa_grouped_yearly, years, n_quantiles=7):
    """
    Function to plot crime rate distribution for each year.

    Parameters:
    lsoa_grouped_yearly: GeoDataFrame with crime data aggregated by LSOA and year
    years: List of years to plot.
    n_quantiles: Number of quantiles to split crime rate distribution.
    """
    # Merge geographic and crime data
    merged_cat = UK_LSOA.merge(lsoa_grouped_yearly, left_on='lsoa21cd', right_on='LSOA Code', how='left')

    # Calculate area-normalized crime rate (crimes per km²) for each LSOA and each year
    for year in years:
        merged_cat[f'{year}_area'] = merged_cat[str(year)] / merged_cat['area_km2']

    # Set layout: 3 columns per row
    ncols = 3
    num_years = len(years)
    nrows = -(-num_years // ncols)  # Ceiling division
    fig, axes = plt.subplots(nrows, ncols, figsize=(ncols * 5, nrows * 4.5))
    axes = axes.flatten()

    # Plot maps for each year
    for i, year in enumerate(years):
        ax = axes[i]
        year_str = f'{year}_area'

        year_data = merged_cat[year_str].dropna()
        _, bins = pd.qcut(year_data, q=n_quantiles, retbins=True, duplicates="drop")
        unique_cutpoints = np.unique(bins.round(2))
        unique_cutpoints = unique_cutpoints[1:-1]

        merged_cat.plot(
            column=year_str, cmap='RdBu_r', linewidth=0, ax=ax,
            legend=True, scheme='user_defined',
            classification_kwds={'bins': unique_cutpoints},
            legend_kwds={
                'fontsize': 9,
                'markerscale': 0.5,
                'loc': 'lower right',
                'frameon': True,
                'title': 'Rate'
            },
            missing_kwds={
                'color': 'lightgrey',
                'hatch': '///',
                'label': 'Missing'
            }
        )

        ax.set_title(f'Total Crime Rate\nLondon LSOAs - {year} (per km²)', fontsize=12)
        ax.axis('off')

    # Remove unused subplots
    for i in range(num_years, len(axes)):
        fig.delaxes(axes[i])

    # Adjust spacing
    plt.subplots_adjust(hspace=0.1, wspace=0.05)
    plt.tight_layout()
    plt.show()

# Timeseries of each Major Category. What are trends of each major category like?
drop_list = ['Major Category', 'Borough']
lsoa_grouped_yearly = df_yearly.groupby(by=['LSOA Code']).agg('sum').drop(columns=drop_list)

lsoa_grouped_yearly.head()

for_all_types(lsoa_grouped_yearly, years)

# Get the median

df_yearly['median'] = df_yearly.loc[:, '2022':'2024'].median(axis=1)
crime_median = df_yearly[["LSOA Code", "Major Category", "median"]]
crime_median.head()

# pivot the table

crime_median = crime_median.pivot_table(index= 'LSOA Code', columns = 'Major Category', 
                                    values = "median", fill_value = 0).reset_index()
#then rename the axis
crime_median.rename_axis(None, axis=1, inplace=True)
crime_median.head()

# calculate all crimes of each lsoa

#extract the columns to sum
cols_to_sum = [col for col in crime_median.columns if "LSOA" not in col]
#sum across the columns rather than rows
crime_median['Total_crime'] = 0
#sum across the columns rather than rows
crime_median["Total_crime"] = crime_median[cols_to_sum].sum(axis=1)
crime_median.head()

# Percentage normalization

for col in cols_to_sum:
    crime_median[col] = (crime_median[col] / crime_median["Total_crime"]).round(4)
crime_median.head()

# merged with geographic data, and calculating crime density (per square kilometer)

london_crime_median = UK_LSOA.merge(crime_median, left_on="lsoa21cd", right_on="LSOA Code", how="right").drop(columns='LSOA Code')
london_crime_median['crime_per_km2'] = london_crime_median['Total_crime'] / london_crime_median['area_km2']
london_crime_median.head()

def plot_dendrogram(model, **kwargs):
    
    counts = np.zeros(model.children_.shape[0])
    n_samples = len(model.labels_)
    for i, merge in enumerate(model.children_):
        current_count = 0
        for child_idx in merge:
            if child_idx < n_samples:
                current_count +=1
            else:
                current_count += counts[child_idx-n_samples]
        counts[i] = current_count
        
    linkage_matrix = np.column_stack([model.children_, model.distances_,
                                    counts]).astype(float)
    
    dendrogram(linkage_matrix, **kwargs)

crime_clus_median = london_crime_median[cols_to_sum]

agg_cluster = AgglomerativeClustering(distance_threshold=0, n_clusters=None).fit(crime_clus_median)

fig, ax = plt.subplots(figsize = (10,10))
ax.set_title("Hierarchical clustering dendrogram")
#plot the top three levels of the dendrogram
plot_dendrogram(agg_cluster, truncate_mode='level', p=5)
plt.axhline(y = 3.9, color = "r", linestyle = "--")
plt.axhline(y = 5, color = "r", linestyle = "--")
plt.axhline(y = 6.6, color = "r", linestyle = "--")
ax.set_xlabel("Number of points in node")
plt.show()

# Perform Agglomerative Clustering with a distance threshold of 5 to obtain 4 clusters
agg_cluster_5 = AgglomerativeClustering(linkage="ward", distance_threshold=5, n_clusters=None)
agg_cluster_5.fit(crime_clus_median)

# Add clustering results to the original dataset
london_crime_median["Aggl_clus"] = agg_cluster_5.labels_

# Define a custom color list (ensure it has at least as many colors as clusters)
custom_colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728"]

# Create a custom colormap based on the number of unique clusters
n_clusters = london_crime_median["Aggl_clus"].nunique()
cmap = ListedColormap(custom_colors[:n_clusters])

# Plot the clustering results on a map
fig, ax = plt.subplots(figsize=(10, 10))
london_crime_median.plot(
    column="Aggl_clus",
    categorical=True,
    legend=True,
    cmap=cmap,
    ax=ax
)

# Hide axes for better visualization
ax.set_axis_off()

agglom_sizes = london_crime_median.groupby("Aggl_clus").size()
agglom_sizes

Aggl_clus
0    2921
1    1127
2     505
3     435
dtype: int64

# Calculate the mean values of the specified columns for each cluster
# Group the data by the "Aggl_clus" column and compute the mean for each cluster
agglom_means = london_crime_median.groupby("Aggl_clus")[cols_to_sum].mean()
agglom_means

# Transpose the DataFrame (swap rows and columns), then round all numerical values to three decimal places.
agglom_means_T = agglom_means.T.round(3)
#agglom_means_T.reset_index(inplace=True)
agglom_means_T = pd.DataFrame(agglom_means_T)
agglom_means_T

agglom_means_T.reset_index(inplace=True)

#create subplots for each cluster
fig, ax = plt.subplots(1,4, figsize = (15,8), sharey = True, sharex = True)
#flatten the axis
axis = ax.flatten()

#going over each column
for i, col in enumerate(agglom_means_T.columns):
    #ignore the index column
    if col != "index":
        ax = axis[i-1]
        #plot the bar chart
        ax.bar(height = agglom_means_T[col], x=agglom_means_T["index"], color = custom_colors[i-1] )
        # set the x-ticks
        ax.set_xticks(range(len(agglom_means_T["index"])))  # Set x-ticks explicitly
        # set the x-tick labels
        ax.set_xticklabels(labels=agglom_means_T["index"], rotation=90)
        #set the title
        ax.set_title(f"Cluster {col}", fontsize = 20)

# Divide 'crime_per_km2' into 8 quantile-based categories
quantiles = london_crime_median['crime_per_km2'].quantile([0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0])
bins = quantiles.values
labels = ['Very Low', 'Low', 'Medium-Low', 'Medium', 'Medium-High', 'High', 'Very High', 'Extremely High']

# Assign crime density categories based on quantile bins
london_crime_median['crime_density_category'] = pd.cut(
    london_crime_median['crime_per_km2'], 
    bins=bins, 
    labels=labels, 
    include_lowest=True
)

# Perform Chi-Square test to check for association between cluster and crime density category
contingency_table = pd.crosstab(london_crime_median['Aggl_clus'], london_crime_median['crime_density_category'])

# Apply Chi-Square test of independence
chi2, p, _, _ = chi2_contingency(contingency_table)

print("Chi-Square p-value:", p)
print("Chi-Square Statistic:", chi2)
print("Degrees of Freedom:", (contingency_table.shape[0] - 1) * (contingency_table.shape[1] - 1))
# print("Contingency Table:\n", contingency_table)

Chi-Square p-value: 0.0
Chi-Square Statistic: 1644.08536410112
Degrees of Freedom: 21

# Visualize chi-square analysis results

plt.figure(figsize=(10, 6))
sns.countplot(data=london_crime_median, x='Aggl_clus', hue='crime_density_category', palette='Set2')

# Set the title and label
plt.title('Crime Density Categories by Agglomerative Clustering', fontsize=16)
plt.xlabel('Agglomerative Clustering Cluster', fontsize=14)
plt.ylabel('Frequency', fontsize=14)
plt.legend(title='Crime Density Category', loc='upper right')

plt.show()

end_time = time.time() 
run_time = end_time - start_time 
print("Total run time: ", run_time, "seconds")

Total run time:  330.52668952941895 seconds

Column Name	Description
`LSOA Code`	A unique identifier for each Lower Layer Super Output Area
`LSOA Name`	The name corresponding to each LSOA
`Borough`	A unique identifier for each Borough in which the LSOA is located
`Major Category`	The broad category of crime
`Minor Category`	The specific subcategory of the crime
Monthly crime counts	Each column represents a month, containing the crime counts for that month

	LSOA Code	Borough	Major Category	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
0	E01000006	E09000002	ARSON AND CRIMINAL DAMAGE	4.0	9.0	5.0	8.0	8.0	8.0	5.0	12.0	7.0	6.0	5.0	6.0	5.0	5.0
1	E01000006	E09000002	BURGLARY	20.0	17.0	14.0	10.0	10.0	7.0	13.0	16.0	13.0	5.0	5.0	3.0	3.0	2.0
2	E01000006	E09000002	DRUG OFFENCES	10.0	1.0	6.0	8.0	6.0	1.0	3.0	14.0	6.0	25.0	16.0	17.0	5.0	3.0
3	E01000006	E09000002	MISCELLANEOUS CRIMES AGAINST SOCIETY	0.0	1.0	1.0	4.0	0.0	1.0	2.0	2.0	4.0	1.0	3.0	5.0	1.0	2.0
4	E01000006	E09000002	POSSESSION OF WEAPONS	0.0	0.0	0.0	1.0	0.0	0.0	1.0	1.0	0.0	0.0	1.0	2.0	0.0	2.0

	lsoa21cd	lsoa21nm	geometry	area_km2
0	E01000001	City of London 001A	POLYGON ((532151.538 181867.433, 532152.5 1818...	0.129865
1	E01000002	City of London 001B	POLYGON ((532634.497 181926.016, 532632.048 18...	0.228420
2	E01000003	City of London 001C	POLYGON ((532153.703 182165.155, 532158.25 182...	0.059054
3	E01000005	City of London 001E	POLYGON ((533619.062 181402.364, 533639.868 18...	0.189578
4	E01000006	Barking and Dagenham 016A	POLYGON ((545126.852 184310.838, 545145.213 18...	0.146537

	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
Major Category
ARSON AND CRIMINAL DAMAGE	75996.0	64730.0	54764.0	57314.0	61020.0	62106.0	61604.0	56324.0	55500.0	49982.0	51781.0	51942.0	56048.0	55138.0
BURGLARY	100836.0	97910.0	87187.0	75886.0	70489.0	68306.0	75975.0	80291.0	80362.0	60216.0	52318.0	51660.0	55434.0	52492.0
DRUG OFFENCES	64931.0	55755.0	50292.0	44400.0	39823.0	39592.0	36528.0	36084.0	47455.0	54571.0	45372.0	43358.0	38284.0	38063.0
MISCELLANEOUS CRIMES AGAINST SOCIETY	8768.0	8130.0	7727.0	8823.0	9979.0	11346.0	11190.0	11005.0	11143.0	11070.0	11130.0	11709.0	11003.0	10081.0
POSSESSION OF WEAPONS	5550.0	4286.0	3992.0	4322.0	4788.0	5843.0	7726.0	7685.0	7257.0	6725.0	5975.0	6278.0	6266.0	4522.0

	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024
LSOA Code
E01000006	107.0	100.0	93.0	73.0	82.0	79.0	95.0	113.0	108.0	103.0	94.0	119.0	87.0	89.0
E01000007	403.0	366.0	312.0	269.0	385.0	370.0	442.0	387.0	519.0	529.0	507.0	473.0	647.0	521.0
E01000008	178.0	130.0	131.0	176.0	196.0	180.0	172.0	194.0	213.0	192.0	224.0	210.0	234.0	135.0
E01000009	337.0	278.0	222.0	234.0	241.0	183.0	189.0	248.0	270.0	292.0	298.0	294.0	376.0	243.0
E01000011	113.0	108.0	81.0	99.0	70.0	108.0	95.0	101.0	91.0	107.0	111.0	103.0	121.0	115.0

A spatiotemporal analysis of the major types of crime in London¶

Preparation¶

Table of contents¶

Introduction¶

Research questions¶

Data¶

Data Description¶

What are Borough and LSOA?¶

Crime Categories¶

Geographic Data¶

Methodology¶

Results and discussion¶

Temporal distribution of crime rate¶

📊 Time Series Analysis Summary¶

Spatial distribution of crime rate¶

🗺️ Spatial Distribution Analysis Summary¶

Cluster analysis¶

Chi-Square Analysis of Crime Rate and Clusters¶

Conclusion¶

Key Findings¶

Significance of the Study¶

Policy Implications¶

References¶

	LSOA Code	ARSON AND CRIMINAL DAMAGE	BURGLARY	DRUG OFFENCES	MISCELLANEOUS CRIMES AGAINST SOCIETY	POSSESSION OF WEAPONS	PUBLIC ORDER OFFENCES	ROBBERY	THEFT	VEHICLE OFFENCES	VIOLENCE AGAINST THE PERSON
0	E01000006	5.0	3.0	5.0	2.0	2.0	6.0	6.0	13.0	21.0	34.0
1	E01000007	32.0	22.0	72.0	2.0	6.0	43.0	39.0	138.0	23.0	156.0
2	E01000008	14.0	13.0	27.0	3.0	3.0	7.0	6.0	27.0	32.0	61.0
3	E01000009	24.0	12.0	57.0	3.0	2.0	15.0	14.0	58.0	22.0	94.0
4	E01000011	5.0	5.0	10.0	4.0	1.0	3.0	5.0	12.0	12.0	54.0

	LSOA Code	ARSON AND CRIMINAL DAMAGE	BURGLARY	DRUG OFFENCES	MISCELLANEOUS CRIMES AGAINST SOCIETY	POSSESSION OF WEAPONS	PUBLIC ORDER OFFENCES	ROBBERY	THEFT	VEHICLE OFFENCES	VIOLENCE AGAINST THE PERSON	Total_crime
0	E01000006	0.0515	0.0309	0.0515	0.0206	0.0206	0.0619	0.0619	0.1340	0.2165	0.3505	97.0
1	E01000007	0.0600	0.0413	0.1351	0.0038	0.0113	0.0807	0.0732	0.2589	0.0432	0.2927	533.0
2	E01000008	0.0725	0.0674	0.1399	0.0155	0.0155	0.0363	0.0311	0.1399	0.1658	0.3161	193.0
3	E01000009	0.0797	0.0399	0.1894	0.0100	0.0066	0.0498	0.0465	0.1927	0.0731	0.3123	301.0
4	E01000011	0.0450	0.0450	0.0901	0.0360	0.0090	0.0270	0.0450	0.1081	0.1081	0.4865	111.0

	ARSON AND CRIMINAL DAMAGE	BURGLARY	DRUG OFFENCES	MISCELLANEOUS CRIMES AGAINST SOCIETY	POSSESSION OF WEAPONS	PUBLIC ORDER OFFENCES	ROBBERY	THEFT	VEHICLE OFFENCES	VIOLENCE AGAINST THE PERSON
Aggl_clus
0	0.083598	0.073982	0.050222	0.015012	0.006333	0.068932	0.024905	0.144295	0.153655	0.379057
1	0.067119	0.084911	0.043106	0.010832	0.005273	0.067980	0.037918	0.277971	0.149845	0.255047
2	0.077545	0.106464	0.031198	0.011291	0.002467	0.048226	0.018956	0.127662	0.336704	0.239482
3	0.044306	0.054785	0.033574	0.006349	0.004565	0.058620	0.041589	0.500651	0.067794	0.187764

Aggl_clus	0	1	2	3
ARSON AND CRIMINAL DAMAGE	0.084	0.067	0.078	0.044
BURGLARY	0.074	0.085	0.106	0.055
DRUG OFFENCES	0.050	0.043	0.031	0.034
MISCELLANEOUS CRIMES AGAINST SOCIETY	0.015	0.011	0.011	0.006
POSSESSION OF WEAPONS	0.006	0.005	0.002	0.005
PUBLIC ORDER OFFENCES	0.069	0.068	0.048	0.059
ROBBERY	0.025	0.038	0.019	0.042
THEFT	0.144	0.278	0.128	0.501
VEHICLE OFFENCES	0.154	0.150	0.337	0.068
VIOLENCE AGAINST THE PERSON	0.379	0.255	0.239	0.188